TraceScope: Tracing Failures from Evidence to Decision in Upper-GI Endoscopy

Upper-gastrointestinal (upper-GI) endoscopy spans clinical tasks from local visual assessment to examination-level interpretation and decision making. During an examination, clinicians localize anatomy, identify abnormalities, and characterize lesion attributes, while integrating distributed observations to formulate reports, diagnostic impressions, and guideline-guided decisions. These tasks require different scopes of visual evidence: local judgments rely on compact, target-relevant views, whereas examination-level interpretation requires integrating distributed and often redundant evidence across the examination. Recent endoscopic benchmarks have substantially broadened clinical task coverage, yet task definition and evidence scope are still commonly evaluated without systematic alignment. To bridge this gap, we introduce TraceScope, a report-grounded multimodal benchmark for upper-GI endoscopy. Its main board contains 9,106 task-specific items from 4,893 examinations and 70,386 item–frame references. A shared clinical specification organizes six task families spanning site recognition, abnormal findings, finding attributes, report generation, diagnostic impression, and guideline reasoning. For each task, the delivered visual context is matched to its evidence requirements in clinical practice, ranging from compact, target-relevant views to redundant examination-level evidence. Across 22 multimodal models, the main board reveals a stage-selective capability profile: performance on local readouts does not extend uniformly to clinical composition, while reference-consistent reasoning remains separable from exact guideline decisions. Two complementary within-case analyses further show that selected multi-image presentation consistently outperforms equal-count uniformly sampled video input, while making guideline-sign polarity explicit yields the largest positive fleet-level change along the clinical-state cascade. Together, TraceScope aligns stage-resolved clinical evaluation with task-appropriate evidence context, enabling unified analysis of model capability across the upper-GI workflow and of downstream decision sensitivity to visual and clinical information.